A neural network-based approach for calculating dissolved oxygen profiles in reservoirs

Soyupak S., Karaer F., Gurbuz H., Kivrak E., Senturk E., Yazici A.

NEURAL COMPUTING & APPLICATIONS, cilt.12, ss.166-172, 2003 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 12
  • Basım Tarihi: 2003
  • Doi Numarası: 10.1007/s00521-003-0378-8
  • Dergi Adı: NEURAL COMPUTING & APPLICATIONS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.166-172
  • Anahtar Kelimeler: dissolved oxygen, generalisation, Levenberg-Marquardt algorithm, neural networks, reservoirs, water quality modelling, FEEDFORWARD NETWORKS
  • Bursa Uludağ Üniversitesi Adresli: Evet

Özet

A Neural Network (NN) modelling approach has been shown to be successful in calculating pseudo steady state time and space dependent Dissolved Oxygen (DO) concentrations in three separate reservoirs with different characteristics using limited number of input variables. The Levenberg-Marquardt algorithm was adopted during training. Pre-processing before training and post processing after simulation steps were the treatments applied to raw data and predictions respectively. Generalisation was improved and over-fitting problems were eliminated: Early stopping method was applied for improving generalisation. The correlation coefficients between neural network estimates and field measurements were as high as 0.98 for two of the reservoirs with experiments that involve double layer neural network structure with 30 neurons within each hidden layer. A simple one layer neural network structure with 11 neurons has yielded comparable and satisfactorily high correlation coefficients for complete data set, and training, validation and test sets of the third reservoir.